These sources collectively discuss the critical issue of political bias in Large Language Models (LLMs) and the various methodologies for its measurement and mitigation. The first academic excerpt proposes a granular, two-tiered framework to measure bias by analyzing both the political stance (what the model says) and framing bias (how the model says it, including content and style), revealing that models often lean liberal but show topic-specific variability. The second academic paper explores the relationship between truthfulness and political bias in LLM reward models, finding that optimizing models for objective truth often unintentionally results in a left-leaning political bias that increases with model size. Finally, the two news articles highlight OpenAI’s recent, sophisticated approach to quantifying political bias using five operational axes of bias (e.g., asymmetric coverage and personal political expression), noting that while overt bias is rare, emotionally charged prompts can still elicit moderate, measurable bias in their latest models.